Event response for a shared pool of configurable computing resources which uses a set of dynamically-assigned resources

ABSTRACT

Disclosed aspects manage a shared pool of configurable computing resources. A triggering event is detected. The triggering event may be related to a resource utilization on a host of the shared pool of configurable computing resources. Based on a set of profile data, it is determined to perform an event response. The event response includes initiating a resource action or initiating an asset action. The resource action may include distributing a set of dynamically-assigned resources. The asset action corresponds to a set of assets (e.g., migrating a set of virtual machines). To change the resource utilization on the host, the event response is performed.

BACKGROUND

This disclosure relates generally to computer systems and, moreparticularly, relates to managing a shared pool of configurablecomputing resources which uses a set of dynamically-assigned resources.The amount of data that needs to be managed by enterprises isincreasing. Management of a shared pool of configurable computingresources may be desired to be performed as efficiently as possible. Asdata needing to be managed increases, the need for management efficiencymay increase.

SUMMARY

Aspects of the disclosure are used to manage a shared pool ofconfigurable computing resources which uses a set ofdynamically-assigned resources with respect to capacity-on-demandtechnology. A triggering event is detected. The triggering event may berelated to a resource utilization on a host of the shared pool ofconfigurable computing resources. Based on a set of profile data, it isdetermined to perform an event response. The event response includesinitiating a resource action or initiating an asset action. The resourceaction may include distributing a set of dynamically-assigned resources.The asset action corresponds to a set of assets (e.g., migrating a setof virtual machines). To change the resource utilization on the host,the event response is performed.

Aspects of the disclosure include using capacity-on-demand technology orlive migration to resolve/balance resource utilization by, for example,considering relevant policies which may be administrator/user-defined.Elements can include a selection of an appropriate action to achieve athreshold resource utilization via mobile resourcedistribution/activation, virtual machine live migration, or both—basedon policies made available to a manager/scheduler. Efficient usage ofcapacity-on-demand resources can provide performance benefits such ashigh availability, for example.

The above summary is not intended to describe each illustratedembodiment or every implementation of the present disclosure.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The drawings included in the present application are incorporated into,and form part of, the specification. They illustrate embodiments of thepresent disclosure and, along with the description, serve to explain theprinciples of the disclosure. The drawings are only illustrative ofcertain embodiments and do not limit the disclosure.

FIG. 1 depicts a cloud computing node according to embodiments.

FIG. 2 depicts a cloud computing environment according to embodiments.

FIG. 3 depicts abstraction model layers according to embodiments.

FIG. 4 is a flowchart illustrating a method for managing a shared poolof configurable computing resources which uses a set ofdynamically-assigned resources with respect to capacity-on-demandtechnology according to embodiments.

FIG. 5 shows an example system having a shared pool of configurablecomputing resources which uses a set of dynamically-assigned resourceswith respect to capacity-on-demand technology according to embodiments.

While the invention is amenable to various modifications and alternativeforms, specifics thereof have been shown by way of example in thedrawings and will be described in detail. It should be understood,however, that the intention is not to limit the invention to theparticular embodiments described. On the contrary, the intention is tocover all modifications, equivalents, and alternatives falling withinthe spirit and scope of the invention.

DETAILED DESCRIPTION

Aspects of the disclosure relate to capacity-on-demand technology whichallows compute servers to have compute resources (e.g., processors,memory) dynamically assigned/activated (e.g., to make efficient use oflicenses). Aspects include using capacity-on-demand technology or livemigration to resolve/balance resource utilization based on a set ofprofile data (e.g., considering relevant policies which may beadministrator/user-defined). Elements can include a selection of anappropriate action to achieve a threshold resource utilization viamobile resource distribution/activation, virtual machine live migration(e.g., which may have burdens such as taking a temporal period such as10-20 minutes to complete), or both—based on policies (e.g., in the setof profile data, associated with workload characteristics) madeavailable to a manager/scheduler. Capacity-on-demand resources can beexpensive for customers and efficient usage of such resources canprovide performance benefits such as high availability, for example.

Aspects of the disclosure include a method, system, and computer programproduct for managing a shared pool of configurable computing resources.A triggering event is detected. The triggering event may be related to aresource utilization on a host of the shared pool of configurablecomputing resources. Based on a set of profile data, it is determined toperform an event response. The event response includes initiating aresource action or initiating an asset action. The resource action mayinclude distributing a set of dynamically-assigned resources. The assetaction corresponds to a set of assets (e.g., migrating a set of virtualmachines). To change the resource utilization on the host, the eventresponse is performed.

In various embodiments, determining to perform the event response basedon the set of profile data includes selecting the resource action inresponse to the set of profile data indicating a first parameter valuefor a parameter, and selecting the asset action in response to the setof profile data indicating a second parameter value for the parameter.In embodiments, the set of profile data includes a selection from agroup consisting of at least one of: a temporal factor, a physicallocation factor, an unassigned resource availability factor, an assignedresource availability factor, a weighted resource availability factor,an asset priority factor, an expected performance factor, a historicalusage factor, or a user-provided factor. In certain embodiments, the setof profile data includes both a set of user input data and a set ofcomputing environment data.

For example, determining to perform the event response based on the setof profile data can include various operations. In response to the setof profile data indicating to avoid use of the set ofdynamically-assigned resources during the temporal period, the assetaction may be selected. The resource action may be selected: in responseto the set of profile data indicating to avoid a virtual machinemigration during the temporal period, in response to the set of profiledata indicating to avoid a virtual machine migration from the host, inresponse to the set of profile data indicating to avoid a virtualmachine migration of the virtual machine from the host, in response tothe set of profile data indicating an unassigned resource value exceedsa threshold unassigned resource value, in response to the set of profiledata indicating an assigned resource value exceeds a target resourcevalue for the donor host, or in response to the set of profile dataindicating an expected burden of executing the asset action exceeds anexpected burden of executing the resource action. Altogether,performance or efficiency benefits when managing a shared pool ofconfigurable computing resources which uses a set ofdynamically-assigned resources may occur (e.g., speed, flexibility,responsiveness, load balancing, availability, resource usage,productivity). Aspects may save resources such as bandwidth, processing,or memory.

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting for loadbalancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 1, a block diagram of an example of a cloudcomputing node is shown. Cloud computing node 100 is only one example ofa suitable cloud computing node and is not intended to suggest anylimitation as to the scope of use or functionality of embodiments of theinvention described herein. Regardless, cloud computing node 100 iscapable of being implemented and/or performing any of the functionalityset forth hereinabove.

In cloud computing node 100 there is a computer system/server 110, whichis operational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 110 include, but are notlimited to, personal computer systems, server computer systems, tabletcomputer systems, thin clients, thick clients, handheld or laptopdevices, multiprocessor systems, microprocessor-based systems, set topboxes, programmable consumer electronics, network PCs, minicomputersystems, mainframe computer systems, and distributed cloud computingenvironments that include any of the above systems or devices, and thelike.

Computer system/server 110 may be described in the general context ofcomputer system executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 110 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 1, computer system/server 110 in cloud computing node100 is shown in the form of a general-purpose computing device. Thecomponents of computer system/server 110 may include, but are notlimited to, one or more processors or processing units 120, a systemmemory 130, and a bus 122 that couples various system componentsincluding system memory 130 to processing unit 120.

Bus 122 represents one or more of any of several types of busstructures, including a memory bus or memory controller, a peripheralbus, an accelerated graphics port, and a processor or local bus usingany of a variety of bus architectures. By way of example, and notlimitation, such architectures include Industry Standard Architecture(ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA)bus, Video Electronics Standards Association (VESA) local bus, andPeripheral Component Interconnect (PCI) bus.

Computer system/server 110 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 110, and it includes both volatileand non-volatile media, removable and non-removable media. An example ofremovable media is shown in FIG. 1 to include a Digital Video Disc (DVD)192.

System memory 130 can include computer system readable media in the formof volatile or non-volatile memory, such as firmware 132. Firmware 132provides an interface to the hardware of computer system/server 110.System memory 130 can also include computer system readable media in theform of volatile memory, such as random access memory (RAM) 134 and/orcache memory 136. Computer system/server 110 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 140 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 122 by one or more datamedia interfaces. As will be further depicted and described below,memory 130 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions described in more detail below.

Program/utility 150, having a set (at least one) of program modules 152,may be stored in memory 130 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 152 generally carry out the functionsand/or methodologies of embodiments of the invention as describedherein.

Computer system/server 110 may also communicate with one or moreexternal devices 190 such as a keyboard, a pointing device, a display180, a disk drive, etc.; one or more devices that enable a user tointeract with computer system/server 110; and/or any devices (e.g.,network card, modem, etc.) that enable computer system/server 110 tocommunicate with one or more other computing devices. Such communicationcan occur via Input/Output (I/O) interfaces 170. Still yet, computersystem/server 110 can communicate with one or more networks such as alocal area network (LAN), a general wide area network (WAN), and/or apublic network (e.g., the Internet) via network adapter 160. Asdepicted, network adapter 160 communicates with the other components ofcomputer system/server 110 via bus 122. It should be understood thatalthough not shown, other hardware and/or software components could beused in conjunction with computer system/server 110. Examples, include,but are not limited to: microcode, device drivers, redundant processingunits, external disk drive arrays, Redundant Array of Independent Disk(RAID) systems, tape drives, data archival storage systems, etc.

Referring now to FIG. 2, illustrative cloud computing environment 200 isdepicted. As shown, cloud computing environment 200 comprises one ormore cloud computing nodes 100 with which local computing devices usedby cloud consumers, such as, for example, personal digital assistant(PDA) or cellular telephone 210A, desktop computer 210B, laptop computer210C, and/or automobile computer system 210N may communicate. Nodes 100may communicate with one another. They may be grouped (not shown)physically or virtually, in one or more networks, such as Private,Community, Public, or Hybrid clouds as described hereinabove, or acombination thereof. This allows cloud computing environment 200 tooffer infrastructure, platforms and/or software as services for which acloud consumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 210A-Nshown in FIG. 2 are intended to be illustrative only and that computingnodes 100 and cloud computing environment 200 can communicate with anytype of computerized device over any type of network and/or networkaddressable connection (e.g., using a web browser).

Referring now to FIG. 3, a set of functional abstraction layers providedby cloud computing environment 200 in FIG. 2 is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 3 are intended to be illustrative only and the disclosure andclaims are not limited thereto. As depicted, the following layers andcorresponding functions are provided.

Hardware and software layer 310 includes hardware and softwarecomponents. Examples of hardware components include mainframes, in oneexample IBM System z systems; RISC (Reduced Instruction Set Computer)architecture based servers, in one example IBM System p systems; IBMSystem x systems; IBM BladeCenter systems; storage devices; networks andnetworking components. Examples of software components include networkapplication server software, in one example IBM Web Sphere® applicationserver software; and database software, in one example IBM DB2® databasesoftware. IBM, System z, System p, System x, BladeCenter, WebSphere, andDB2 are trademarks of International Business Machines Corporationregistered in many jurisdictions worldwide.

Virtualization layer 320 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers;virtual storage; virtual networks, including virtual private networks;virtual applications and operating systems; and virtual clients.

In one example, management layer 330 may provide the functions describedbelow. Resource provisioning provides dynamic procurement of computingresources and other resources that are utilized to perform tasks withinthe cloud computing environment. Metering and Pricing provide costtracking as resources are utilized within the cloud computingenvironment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal provides access to the cloud computing environment forconsumers and system administrators. Service level management providescloud computing resource allocation and management such that requiredservice levels are met. Service Level Agreement (SLA) planning andfulfillment provide pre-arrangement for, and procurement of, cloudcomputing resources for which a future requirement is anticipated inaccordance with an SLA. A cloud manager 350 is representative of a cloudmanager (or shared pool manager) as described in more detail below.While the cloud manager 350 is shown in FIG. 3 to reside in themanagement layer 330, cloud manager 350 can span all of the levels shownin FIG. 3, as discussed below.

Workloads layer 340 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation; software development and lifecycle management; virtualclassroom education delivery; data analytics processing; transactionprocessing; and a set of dynamically-assigned resources 360, which maybe used as discussed in more detail below.

FIG. 4 is a flowchart illustrating a method 400 for managing a sharedpool of configurable computing resources which uses a set ofdynamically-assigned resources with respect to capacity-on-demandtechnology according to embodiments. The shared pool of configurablecomputing resources may utilize a shared pool manager (e.g., acontroller, a cloud manager) to execute/carry-out processes/tasks. Theshared pool manager may or may not be included in the shared pool ofconfigurable computing resources.

Capacity-on-demand technology can allow compute servers to have computeresources (e.g., processors, memory) dynamically assigned/activated (tomake efficient use of licenses/costs). Capacity-on-demand technology caninclude built-in hardware resources which can be switched on online andwithout an interrupt either temporarily or permanently. The set ofdynamically-assigned resources (e.g., processors, memory) may bereferred to as mobile resources (e.g., non-dedicated resource licenses)which can be allocated to various hosts in response to a triggeringevent (e.g., as needed/desired/requested). Method 400 may begin at block401.

In embodiments, an x86 processor is absent with respect to the set ofdynamically-assigned resources at block 404. x86 processors may utilizesoftware hypervisors for virtualization. x86 processors can haveadditional layers with respect to non-x86 processors. In certainembodiments, support for a hypervisor is built into the chip (e.g.,embedded firmware managing the processor and memory resources).Accordingly, the hypervisor may run as a piece of firmware codeinteracting with the hardware and virtual machines.

In embodiments, a resource manager may be used at block 406 to manage aset of operations in an automated fashion without user intervention asdescribed herein (e.g., detecting a triggering event, determining toperform an event response based on a set of profile data, performing theevent response). The resource manager may be included in the shared poolmanager, or may be separate. As such, the resource manager can managecapacity-on-demand resources such as the set of dynamically-assignedresources (e.g., mobile/floating processors, mobile/floating memory).

At block 410, a triggering event is detected. The triggering event maybe related to a resource utilization (e.g., amount of processing/memorycapability being used, 80%, 20 cores, 16 gigabytes) on a host of theshared pool of configurable computing resources. Detection may occurusing a monitoring application or service, and may collect or scan a setof statistics. In embodiments, the triggering event includes theresource utilization exceeding a threshold resource utilization (e.g.,using 90% of memory capacity when the threshold is 80%, using 100 coreswhen the threshold is 99 cores) at block 421. In various embodiments,the host can include a set of assets. Also, the set of assets caninclude a set of virtual machines. As such, the triggering event mayoccur when the host becomes too busy (e.g., under-resourced toadequately serve its resident virtual machines).

At block 450, it is determined to perform an event response. Thedetermination is based on a set of profile data. The event responseincludes initiating a resource action corresponding to a set ofdynamically-assigned resources or initiating an asset actioncorresponding to a set of assets. The determination can includecomputing/calculating whether to initiate the resource action (e.g.,using capacity-on-demand resources) or whether to initiate the assetaction (e.g., live migration). As such, both event responses may beconsidered. In general, the event response may include an operationwhich addresses/targets the triggering event. Determining can includeascertaining, identifying, resolving, formulating, or computing.Initiating can include starting, instantiating, or beginning in order toexecute, perform, run, or carry-out. For instance, initiating the assetaction can start a migration operation.

The set of profile data can include various factors (e.g., inputs,values, statistics, data-points, free text) which may be analyzed. Forinstance, analyzing can include extracting (e.g., creating aderivation), examining (e.g., performing an inspection), scanning (e.g.,reviewing a sample), evaluating (e.g., generating an appraisal),dissecting (e.g., scrutinizing an attribute), resolving (e.g.,ascertaining an observation/conclusion/answer), parsing (e.g.,deciphering a construct), querying (e.g., asking a question), searching(e.g., exploring for a reason/ground/motivation), comparing (e.g.,relating an assessment), classifying (e.g., assigning a designation), orcategorizing (e.g., organizing by a feature). Data analysis may includea process of inspecting, cleaning, transforming, or modeling data todiscover useful information, suggest conclusions, or support decisions.Data analysis can extract information/patterns from a data set andtransform/translate it into an understandable structure (e.g., a datareport which can be provided/furnished) for further use. The set ofprofile data may be stored/collected in connection with the resourcemanager such as in a database (e.g., a centralized managementapparatus). In certain embodiments, individual hosts may have the set ofprofile data stored/collected locally (e.g., individual hosts may benarrowly tailored/configured by a cloud administrator). Other hybridcombinations of storage/collection of the set of profile data are alsoconsidered.

In embodiments, the set of profile data may be associated with one ormore parameters. The parameters can be populated with parameters values.The resource action may be selected in response to the set of profiledata indicating a first parameter value for a parameter. The assetaction may be selected in response to the set of profile data indicatinga second parameter value for the parameter. For example, the parametermay be “AM/PM.” If the parameter value for the parameter indicates “AM,”then the resource action may be selected/chosen. If the parameter valuefor the parameter indicates “PM,” then the asset action may beselected/chosen. Such selection may also be based on other parameterswhich can be linked with factors described herein.

In embodiments, the set of profile data includes a temporal factor atblock 451. The temporal factor can include months of the year, days ofthe week, hours of the day, selected minutes of an hour (e.g.,top/bottom of the hour), time-frames before/during/after other events,or seasonal elements. For example, peak usage hours may indicate whetherto perform a resource action or an asset action. Also, multiple assetactions within a time-frame (e.g., 30 minutes) may be discouraged (e.g.,with respect to one host or one virtual machine). During busy-seasons(e.g., spring/fall for corn/soybean farmers), resource actions may bepreferred while, during off-seasons (e.g., July/August for ice hockeycoaches), asset actions may be favored. Such seasonal natures (or otherfactors described herein) may be user-provided, be machine-learned, orbe gathered from a corpus. As such, in response to the set of profiledata indicating to avoid a virtual machine migration during a temporalperiod, the resource action may be selected (with respect to thetemporal period during which to perform the event response). In responseto the set of profile data indicating to avoid use of the set ofdynamically-assigned resources during the temporal period, the assetaction may be selected (with respect to a temporal period during whichto perform the event response). Other possible temporal factors areconsidered.

In embodiments, the set of profile data includes a physicallocation/geographic/environment factor at block 452. The physicallocation factor may be related to physical distance between hosts,physical distance between data to be processed, comparative energy usageat different physical locations, etc. For example, an asset actionincluding a virtual machine migration in a data center exclusively inChicago may be preferred relative to using dynamically-assignedresources. However, if the asset action were to require the virtualmachine migration to occur from a first data center in New York to asecond data center in San Francisco, using dynamically-assignedresources may be preferred. Similarly, energy usage at a data centernear a wind-farm on a windy day in Oklahoma may indicate a preferencefor an asset action (e.g., conserve dynamically-assigned resources).Whereas with all other aspects the same, energy usage at a data centernext to a hospital on a hot day in the center of Los Angeles mayindicate a preference for the resource action. Other possiblelocation/geographic/environment factors are considered.

In embodiments, the set of profile data includes an unassigned resourceavailability factor at block 453. The unassigned resource availabilityfactor may indicate how many dynamically-assigned resources areunassigned to other hosts (e.g., available in a pool). If relatively fewmobile resources are available/accessible/gettable, the asset action maybe more prone to be initiated. If a relatively large number of mobileresources are available, the resource action may be initiated.Accordingly, in response to the set of profile data indicating anunassigned resource value (with respect to the set ofdynamically-assigned resources) exceeds a threshold unassigned resourcevalue, the resource action may be selected. As with the other factors,the unassigned resource availability may be combined or used inconjunction with one or more factors including those described herein.

In embodiments, the set of profile data includes an assigned resourceavailability factor at block 454. The assigned resource availabilityfactor may indicate how many dynamically-assigned resources areavailable/accessible/gettable on another host which could bemoved/transitioned. Also, the assigned resource availability factor canindicate burdens/costs (and possibly benefits) execute thetransition/move. For example, another host may have a surplus ofdynamically-assigned resources and can relatively easily donate at leasta portion of the surplus (e.g., basically without a burden to the host).To illustrate a greater burden, perhaps another host has a surplus of 1of its 2 mobile cores. Reducing that host to 1 mobile core may still(barely) meet its utilization needs, but could pose otherperformance/efficiency potential challenges. A variety of burdens/costsmay be considered (e.g., operational costs/burdens oftransitioning/moving resources). As such (e.g., with respect to both theset of dynamically-assigned resources and a donor host), in response tothe set of profile data indicating an assigned resource value exceeds atarget resource value for the donor host, the resource action may beselected.

In embodiments, the set of profile data includes a weighted resourceavailability factor at block 455. For instance, varying weights may begiven to dynamically-assigned resources (e.g., unassigned to hosts,assigned to a first group of hosts, assigned to a second group ofhosts). Also, varying weights may be given to virtual machines ondifferent hosts (e.g., a first virtual machine on a first host, a secondvirtual machine on the first host, a third virtual machine on a secondhost, a fourth virtual machine on the second host). The weights may beutilized to indicate one or more burdens of transitioning/movingresources or migrating virtual machines (or with respect to particularhosts). For example, migrating the first virtual machine from the firsthost may have a burden score of 95 out of 100 while migrating the fourthvirtual machine from the second host may have a burden score of 15 outof 100. Similarly, moving a mobile core from the first group of hostsmay have a burden score of 80 out of 100 while moving a mobile core fromthe second group of hosts may have a burden score of 35 out of 100.Burdens with respect to both (candidate) resource actions and(candidate) asset actions may be compared/evaluated/assessed. Inresponse to the set of profile data indicating an expected burden ofexecuting the asset action exceeds an expected burden of executing theresource action, the resource action may be selected.

In embodiments, the set of profile data includes an asset priorityfactor at block 456. For example, certain hosts, certain virtualmachines, or certain workloads may have an indicated priority so as tonot be impacted be a resource action or an asset action. Others may havean indicated priority to easily enable resource actions or asset actions(e.g., perhaps similar to the weighted resource availability factor).The asset priority factor may have a user-defined/input component. Forexample with respect to the host, in response to the set of profile dataindicating to avoid a virtual machine migration from the host, theresource action may be selected. For instance with respect to a virtualmachine on the host, in response to the set of profile data indicatingto avoid a virtual machine migration of the virtual machine from thehost, the resource action can be selected. In certain embodiments, thepriority may not be an absolute priority. To illustrate with respect toa workload of a virtual machine on the host, in response to both the setof profile data indicating to avoid a virtual machine migration of thevirtual machine from the host and a determination that the resourceaction is impractical based on a group of factors (e.g., factorsdescribed herein), the asset action may be selected (e.g., migrating thevirtual machine nonetheless).

In embodiments, the set of profile data includes an expected performancefactor at block 457. The expected performance factor may include aforecast or prediction for performance (e.g., processor utilization) ofone or more hosts in response to the event response. For instance, thecosts/burdens of performing a migration, downtime, and impact to thevirtual machines may be indicated with respect to a prospective assetaction. Similarly, various resource utilization indicators may be usedfor varying arrangements of the set of dynamically-assigned resources.Other possibilities are considered, and the expected performance factormay be used in conjunction with other factors described herein.

In embodiments, the set of profile data includes a historical usagefactor at block 458. The historical usage factor may include statistics,results, or other information related to past event responses. Forinstance, the historical usage factor may indicate that a particularhost causes great burdens when migrating virtual machines from it. Asanother example, a certain workload may respond more favorably toadditional mobile cores than to being migrated to another host. Thehistorical usage factors may be environment-wide or specific to certainhosts. In general, the historical usage factors may also provideestimates for costs, burdens, or other event response consequences.

In embodiments, the set of profile data includes a user-provided factorat block 459. The user-provided factor can include information orselections related to various other factors described herein. Forexample, the user-provided factor may include weights for when tochoose/select certain operations. Users may also define their ownparameters, and associated parameter values to generate the resultingresource combinations which are preferred. For example, aprocessor-related resource action may take preference over a virtualmachine migration asset action which may take preference over amemory-related resource action. Various possibilities for theuser-provided factor are contemplated.

In embodiments, the set of profile data includes a set of user inputdata and a set of computing environment data at block 460. The set ofuser input data may be similar to the user-provided factor. The set ofuser input data can include features related to the computingenvironment or the shared pool of configurable computing resources thatmay be challenging for the resource manager to discover on its own(e.g., more processing power or memory is about to be added). The set ofcomputing environment data may include information about the computingenvironment which is discovered by the resource manager (e.g., hardware,software, operating statistics). The set of computing environment datamay include measurements of input-output, bandwidth, or other features.

At block 490, the event response is performed. The event response may beperformed to change the resource utilization on the host. Performing theevent response can include initiating/executing the resource action,initiating/executing the asset action, or both. In embodiments, theevent response (e.g., including initiating/executing the resourceaction) includes distributing the set of dynamically-assigned resourcesto the host at block 470. For example, allocating more cores to the hostmay reduce the resource utilization on the host (e.g., adding 10 mobilecores to a host previously having 10 cores may double the resourcecapacity and cut the resource utilization in half to a level below thethreshold, 9/10=90% while 9/20=45% where the threshold resourceutilization may be 50%).

In embodiments, the event response (e.g., including initiating/executingthe asset action) includes migrating a set of virtual machines from thehost to a new host (e.g., the new host has lesser resource utilizationchallenges than the host, the overall environment may operate moreefficiently, moving the set of virtual machines from the host reducesthe load on the host) at block 480. In such embodiments, the eventresponse can include distributing the set of dynamically-assignedresources to the new host (e.g., migrating a virtual machine to the newhost and distributing dynamically-assigned resources to the new host topositively impact resource utilization). In certain embodiments, inresponse to distributing the set of dynamically-assigned resources to,the set of dynamically-assigned resources is activated (e.g., turned-on,made available for use, a restriction/limitation is removed). Activationmay occur without disrupting other resources on the host or the newhost. The activated set of dynamically-assigned resources can receivejobs, workloads, or tasks in response to activation (e.g., before orwith priority relative to other resources on the host or the new host).In certain embodiments, an indication that the second host includes theset of dynamically-assigned resources is recorded in the set of resourceassignment data (e.g., coupling in a record a host identifier and amobile resource identifier for the set of dynamically-assignedresources). In such embodiments, historical data may be recorded toindicate previous locations of dynamically-assigned resources (e.g.,coupling in a historical record a first host identifier and the mobileresource identifier for the set of dynamically-assigned resources).

In embodiments, a usage assessment may be generated with respect to thecapacity-on-demand technology. Use of the set of dynamically-assignedresources may be metered at block 497. For example, mobileprocessors/memory allocated may be measured based on factors such asquantity allocated, temporal periods of allocation, actual usage,available usage, etc. Such factors may correlate to charge-back or costburdens which can be defined in-advance (e.g., utilizing usage tiers) orscaled with respect to a market-rate. An invoice or bill presenting theusage, rendered services, fee, and other payment terms may be generatedbased on the metered use at block 498. The generated invoice may beprovided (e.g., displayed in a dialog box, sent or transferred bye-mail, text message, traditional mail) to the user for notification,acknowledgment, or payment.

Method 400 concludes at block 499. Aspects of method 400 may provideperformance or efficiency benefits for managing a shared pool ofconfigurable computing resources. For example, aspects of method 400 mayhave positive impacts when using a set of dynamically-assigned resourceswith respect to capacity-on-demand technology. Altogether, performanceor efficiency benefits for utilization of the set ofdynamically-assigned resources may occur (e.g., speed, flexibility,responsiveness, load balancing, availability, resource usage,productivity).

FIG. 5 shows an example system 500 having a shared pool of configurablecomputing resources which uses a set of dynamically-assigned resourceswith respect to capacity-on-demand technology according to embodiments.In embodiments, method 400 may be implemented using aspects describedwith respect to the example system 500. As such, aspects of thediscussion related to FIG. 4 and method 400 may be used or applied inthe example system 500. Components depicted in FIG. 5 need not bepresent, utilized, or located as such in every such similar system, andsuch components are presented as an illustrative example. Aspects ofexample system 500 may be implemented in hardware, software or firmwareexecutable on hardware, or a combination thereof. The example system 500may include the shared pool of configurable computing resources (e.g.,the cloud environment). Of course, example system 500 could include manyother features or functions known in the art that are not shown in FIG.5.

A shared pool manager 570 can include a resource manager 571 which has aset of resource assignment data 572. In various embodiments, at leastone of the shared pool manager, the resource manager, or the resourceassignment data a separate from one another. Such aspects cancommunicate with a set of hosts via network 590. The first host 510 mayinclude a first set of processors (P1) 511 (e.g., representing 64processor cores), a second set of processors (P2) 512, a third set ofprocessors (P3) 513, a fourth set of processors (P4) 514, a first set ofmemory (M1) 516 (e.g., representing 64 memory elements), a second set ofmemory (M2) 517, a third set of memory (M3) 518, and a fourth set ofmemory (M4) 519. The second host 520, third host 530, fourth host 540,and fifth host 550 may be configured similarly (e.g., with respect toprocessors 521, 522, 523, 524, 531, 532, 533, 534, 541, 542, 543, 544,551, 552, 553, 554 and memory 526, 527, 528, 529, 536, 537, 538, 539,546, 547, 548, 549, 556, 557, 558, 559).

Capacity-on-demand technology allows hosts to have compute resources(e.g., processors, memory) dynamically activated (e.g., for efficiencyof license costs). Consider example system 500 having 256 physical coresper host. However, a user's typical operational load may only generallyrequire 500 of those cores active. As such, the user only licenses thesystem to run 500 cores which saves licensing fees associated with theremaining 156 cores per host (or 780 in total).

Based on historical information (e.g., past experience), a user maydesire to account for peak temporal periods in the user's environmentwhere the user requires additional processor capacity to meet workloaddemands. However, that extra capacity does not always need to beactivated. As such, capacity-on-demand technology may be applied. Mobilecores (e.g., dynamically-assigned processors) may be utilized/purchased.The mobile cores can be dynamically-assigned one or more hosts. Forexample, the user may implement a group of 320 mobile core licenses. Thegroup can be spread across the user's hosts in a user-defined manner. Assuch, benefits/savings may result compared to having to permanentlylicense all of these cores (because they are rarely all needed at once).Also, the mobile cores may be assigned according to predetermined oruser-defined methodologies (e.g., 50 to the first host, 108 to thesecond host, 108 to the third host, and 54 to the fourth host).

In cloud management solutions, ongoing performance impact operations canbe enabled to keep the resources across a group of hosts in the cloudenvironment balanced. For example, if processor utilization reaches apredefined threshold on a host, virtual machines may be migrated off ofthe original host to another host until the processor utilization isbelow the threshold on the original host. Defining which resources arekept in balance and how the balancing occurs may be made customizable toa user. Example configurable features include which hosts to keep inbalance, the frequency at which to monitor the hosts, and variousthresholds at which resources may be considered out of balance. Aspectsof the disclosure may have performance or efficiency benefits onresource utilization by using a policy-driven methodology to resolvewhen to distribute/activate dynamically-assigned resources to positivelyimpact resource utilization and when to migrate virtual machines off ofthe host to positively impact resource utilization (e.g., using a set ofprofile data).

In embodiments, a policy-based approach described herein uses variousfactors. A temporal factor can include a time of day (e.g., duringoff-peak hours live migrations may be used regardless of temporal burdenin order to conserve our dynamically-assigned resources for peak hours).An unassigned resource availability factor can include an amount ofdynamically-assigned resources which are available in a pool (e.g.,unassigned to other hosts). An assigned resource availability factor caninclude an amount of dynamically-assigned resources which are availableon another host that could be moved. Costs/burdens of executing themove/transition may be accounted-for, computed, or weighted. An assetpriority factor can use availability requirements of workloads being run(e.g., on higher-priority workloads, a user/administrator may provide aparameter value so as to not to subject it to potential errors due tolive migration and provide a preference for capacity-on-demandactivation so as to favor using dynamically-assigned resources but allowlive migration if such a resource action presents various challenges, isimpractical, or is impossible. An expected performance factor mayindicate the cost/burden of performing a migration, downtime, and impactto the virtual machine.

Disclosed aspects use statistics collection such as daemon taskscollecting network utilization using nicstat or comparable tooling tocollect network utilization information, application program interfaceswith respect to capacity-on-demand support to retrieve available mobileresources or properties/tags with respect to virtual machines (e.g.,availability requirements, workload priority/importance). A costingmodel can be used to determine the cost of performing migration in orderto free up the required resources.

Disclosed aspects enable a cloud administrator to define policies whichmay be used for operations described herein. Calculations may be madewhich positively impact the usage of units of dynamically-assignedresources by virtual machines with higher availability priority, whilealso positively impacting migration burdens (e.g., by consideringconstraints for temporal periods in which migrations are allowed). Assuch, linear programming can be used to perform such calculations;however, other approaches are possible. Altogether, aspects may haveperformance or efficiency benefits relative to x86 systems, relative toalways migrating a virtual machine, or relative to technologies whichutilize a human interface for detecting the triggering event,determining the perform the event response based on the set of profiledata, or performing the event response.

In addition to embodiments described above, other embodiments havingfewer operational steps, more operational steps, or differentoperational steps are contemplated. Also, some embodiments may performsome or all of the above operational steps in a different order. Themodules are listed and described illustratively according to anembodiment and are not meant to indicate necessity of a particularmodule or exclusivity of other potential modules (or functions/purposesas applied to a specific module).

In the foregoing, reference is made to various embodiments. It should beunderstood, however, that this disclosure is not limited to thespecifically described embodiments. Instead, any combination of thedescribed features and elements, whether related to differentembodiments or not, is contemplated to implement and practice thisdisclosure. Many modifications and variations may be apparent to thoseof ordinary skill in the art without departing from the scope and spiritof the described embodiments. Furthermore, although embodiments of thisdisclosure may achieve advantages over other possible solutions or overthe prior art, whether or not a particular advantage is achieved by agiven embodiment is not limiting of this disclosure. Thus, the describedaspects, features, embodiments, and advantages are merely illustrativeand are not considered elements or limitations of the appended claimsexcept where explicitly recited in a claim(s).

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java, Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

Embodiments according to this disclosure may be provided to end-usersthrough a cloud-computing infrastructure. Cloud computing generallyrefers to the provision of scalable computing resources as a serviceover a network. More formally, cloud computing may be defined as acomputing capability that provides an abstraction between the computingresource and its underlying technical architecture (e.g., servers,storage, networks), enabling convenient, on-demand network access to ashared pool of configurable computing resources that can be rapidlyprovisioned and released with minimal management effort or serviceprovider interaction. Thus, cloud computing allows a user to accessvirtual computing resources (e.g., storage, data, applications, and evencomplete virtualized computing systems) in “the cloud,” without regardfor the underlying physical systems (or locations of those systems) usedto provide the computing resources.

Typically, cloud-computing resources are provided to a user on apay-per-use basis, where users are charged only for the computingresources actually used (e.g., an amount of storage space used by a useror a number of virtualized systems instantiated by the user). A user canaccess any of the resources that reside in the cloud at any time, andfrom anywhere across the Internet. In context of the present disclosure,a user may access applications or related data available in the cloud.For example, the nodes used to create a stream computing application maybe virtual machines hosted by a cloud service provider. Doing so allowsa user to access this information from any computing system attached toa network connected to the cloud (e.g., the Internet).

Embodiments of the present disclosure may also be delivered as part of aservice engagement with a client corporation, nonprofit organization,government entity, internal organizational structure, or the like. Theseembodiments may include configuring a computer system to perform, anddeploying software, hardware, and web services that implement, some orall of the methods described herein. These embodiments may also includeanalyzing the client's operations, creating recommendations responsiveto the analysis, building systems that implement portions of therecommendations, integrating the systems into existing processes andinfrastructure, metering use of the systems, allocating expenses tousers of the systems, and billing for use of the systems.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

While the foregoing is directed to exemplary embodiments, other andfurther embodiments of the invention may be devised without departingfrom the basic scope thereof, and the scope thereof is determined by theclaims that follow. The descriptions of the various embodiments of thepresent disclosure have been presented for purposes of illustration, butare not intended to be exhaustive or limited to the embodimentsdisclosed. Many modifications and variations will be apparent to thoseof ordinary skill in the art without departing from the scope and spiritof the described embodiments. The terminology used herein was chosen toexplain the principles of the embodiments, the practical application ortechnical improvement over technologies found in the marketplace, or toenable others of ordinary skill in the art to understand the embodimentsdisclosed herein.

1. A system for managing a shared pool of configurable computingresources, the system comprising: a memory having a set of computerreadable computer instructions, and a processor for executing the set ofcomputer readable instructions, the set of computer readableinstructions including: detecting a triggering event related to aresource utilization on a host of the shared pool of configurablecomputing resources; determining, based on a set of profile data, toperform an event response which is selected from a group consisting ofat least one of: selecting, with respect to a temporal period duringwhich to perform the event response, a resource action in response tothe set of profile data indicating to avoid a first virtual machinemigration during the temporal period. selecting, with respect to thetemporal period during which to perform the event response, an assetaction in response to the set of profile data indicating to avoid use ofa set of dynamically-assigned resources during the temporal period.selecting, with respect to a workload of a virtual machine on the host,the asset action in response to both: the set of profile data indicatingto avoid a second virtual machine migration of the virtual machine fromthe host, and a determination that the resource action is impracticalbased on a group of factors, selecting, with respect to both the set ofdynamically-assigned resources and a donor host, the resource action inresponse to the set of profile data indicating an assigned resourcevalue exceeds a target resource value for the donor host, and selecting,with respect to the resource action and the asset action, the resourceaction in response to the set of profile data indicating an expectedburden of executing the asset action exceeds an expected burden ofexecuting the resource action; and performing the event response tochange the resource utilization on the host.
 2. The system of claim 1,wherein: the triggering event includes the resource utilizationexceeding a threshold resource utilization; the host includes [[the]]aset of assets; the set of assets include a set of virtual machines; anx86 processor is absent with respect to the set of dynamically-assignedresources; and detecting, determining, and performing each occur in anautomated fashion without user intervention.
 3. The system of claim 1,wherein the event response includes distributing the set ofdynamically-assigned resources to the host.
 4. The system of claim 1,wherein the event response includes migrating a set of virtual machinesfrom the host to a new host.
 5. The system of claim 1, whereindetermining, based on the set of profile data, to perform the eventresponse includes: computing whether to initiate the resource action orwhether to initiate the asset action.
 6. The system of claim 1, whereinthe set of profile data includes a selection from a group consisting ofat least one of: a temporal factor; a physical location factor; anunassigned resource availability factor; an assigned resourceavailability factor; a weighted resource availability factor; an assetpriority factor; an expected performance factor; a historical usagefactor; and a user-provided factor.
 7. The system of claim 1, whereinthe set of profile data includes both: a set of user input data, and aset of computing environment data.
 8. The system of claim 1, whereindetermining, based on the set of profile data, to perform the eventresponse includes: selecting the resource action in response to the setof profile data indicating a first parameter value for a parameter, andselecting the asset action in response to the set of profile dataindicating a second parameter value for the parameter.
 9. The system ofclaim 1, wherein determining, based on the set of profile data, toperform the event response includes: selecting, with respect to thetemporal period during which to perform the event response, the resourceaction in response to the set of profile data indicating to avoid thefirst virtual machine migration during the temporal period.
 10. Thesystem of claim 1, wherein determining, based on the set of profiledata, to perform the event response includes: selecting, with respect tothe temporal period during which to perform the event response, theasset action in response to the set of profile data indicating to avoiduse of the set of dynamically-assigned resources during the temporalperiod.
 11. The system of claim 1, wherein determining, based on the setof profile data, to perform the event response includes: selecting, withrespect to the host, the resource action in response to the set ofprofile data indicating to avoid a third virtual machine migration fromthe host.
 12. The system of claim 1, wherein determining, based on theset of profile data, to perform the event response includes: selecting,with respect to a virtual machine on the host, the resource action inresponse to the set of profile data indicating to avoid a fourth virtualmachine migration of the virtual machine from the host.
 13. The systemof claim 1, wherein determining, based on the set of profile data, toperform the event response includes: selecting, with respect to theworkload of the virtual machine on the host, the asset action inresponse to both: the set of profile data indicating to avoid the secondvirtual machine migration of the virtual machine from the host, and thedetermination that the resource action is impractical based on the groupof factors.
 14. The system of claim 1, wherein determining, based on theset of profile data, to perform the event response includes: selecting,with respect to the set of dynamically-assigned resources, the resourceaction in response to the set of profile data indicating an unassignedresource value exceeds a threshold unassigned resource value.
 15. Thesystem of claim 1, wherein determining, based on the set of profiledata, to perform the event response includes: selecting, with respect toboth the set of dynamically-assigned resources and the donor host, theresource action in response to the set of profile data indicating theassigned resource value exceeds the target resource value for the donorhost.
 16. The system of claim 1, wherein determining, based on the setof profile data, to perform the event response includes: selecting, withrespect to the resource action and the asset action, the resource actionin response to the set of profile data indicating the expected burden ofexecuting the asset action exceeds the expected burden of executing theresource action.
 17. The system of claim 1, further comprising: meteringuse of the event response; and generating an invoice based on themetered use.
 18. A computer program product for managing a shared poolof configurable computing resources, the computer program productcomprising a computer readable storage medium having programinstructions embodied therewith, wherein the computer readable storagemedium is not a transitory signal per se, the program instructionsexecutable by a processor to cause the processor to perform a methodcomprising: detecting a triggering event related to a resourceutilization on a host of the shared pool of configurable computingresources; determining, based on a set of profile data, to perform anevent response which is selected from a group consisting of at least oneof: selecting, with respect to a temporal period during which to performthe event response, a resource action in response to the set of profiledata indicating to avoid a first virtual machine migration during thetemporal period. selecting, with respect to the temporal period duringwhich to perform the event response, an asset action in response to theset of profile data indicating to avoid use of a set ofdynamically-assigned resources during the temporal period. selecting,with respect to a workload of a virtual machine on the host, the assetaction in response to both: the set of profile data indicating to avoida second virtual machine migration of the virtual machine from the host,and a determination that the resource action is impractical based on agroup of factors. selecting, with respect to both the set ofdynamically-assigned resources and a donor host, the resource action inresponse to the set of profile data indicating an assigned resourcevalue exceeds a target resource value for the donor host, and selecting,with respect to the resource action and the asset action, the resourceaction in response to the set of profile data indicating an expectedburden of executing the asset action exceeds an expected burden ofexecuting the resource action: and performing the event response tochange the resource utilization on the host.
 19. The computer programproduct of claim 18, wherein at least one of: the program instructionsare stored in the computer readable storage medium in a data processingsystem, and wherein the program instructions were downloaded over anetwork from a remote data processing system the program instructionsare stored in the computer readable storage medium in a server dataprocessing system, and wherein the program instructions are downloadedover a network to the remote data processing system for use in a secondcomputer readable storage medium with the remote data processing system.20. (canceled)
 21. A system for managing a shared pool of configurablecomputing resources, the system comprising: a memory having a set ofcomputer readable computer instructions, and a processor for executingthe set of computer readable instructions, the set of computer readableinstructions including: detecting a triggering event related to aresource utilization on a host of the shared pool of configurablecomputing resources; determining, based on a set of profile data, toperform an event response which includes: selecting, with respect to aresource action and an asset action, the asset action in response to theset of profile data indicating an expected burden of executing theresource action exceeds an expected burden of executing the assetaction; and performing the event response to change the resourceutilization on the host.